Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions

Rajendran, Pavithra, Bollegala, Danushka ORCID: 0000-0003-4476-7003 and Parsons, Simon
(2018) Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions. In: 11th International Conference on Language Resources and Evaluation, 2018-5-7 - 2018-5-12, Miyazaki, Japan.

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Computational argumentation aims to model arguments as a set of premises that either support each other or collectively support a conclusion. We prepare three datasets of text-hypothesis pairs with support-based entailment based on opinions present in hotel reviews using a distant supervision approach. Support-based entailment is defined as the existence of a specific opinion (premise) that supports as well as entails a more general opinion and where these together support a generalised conclusion. A set of rules is proposed based on three different components - sentiment, stance and specificity to automatically predict support-based entailment. Two annotators manually annotated the relations among text-hypothesis pairs with an inter-rater agreement of 0.80. We compare the performance of the rules which gave an overall accuracy of 0.83. Further, we compare the performance of textual entailment under various conditions. The overall accuracy was 89.54%, 90.00% and 96.19% for our three datasets.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: argument mining, stance classification, structured argumentation
Depositing User: Symplectic Admin
Date Deposited: 09 Jan 2018 09:21
Last Modified: 19 Jan 2023 06:46
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